Refine
Document Type
- Article (4)
Language
- English (4)
Has Fulltext
- yes (4)
Keywords
- Benchmark (2)
- Black-Box (2)
- Dataset (2)
- Dynamics (2)
- Industrial Robotics (2)
- Nonlinear Identification (2)
- System Identification (2)
- Neural Adaptive Control (1)
- Neural Networks (1)
- ROS (1)
Faculty / Organisational entity
We present an identification benchmark data set for a full robot movement with an KUKA KR300 R2500 ultra SE industrial robot. It is a robot with a nominal payload capacity of 300 kg, a weight of 1120 kg and a reach of 2500mm. It exhibits 12 states accounting for position and velocity for each of the 6 joints. The robot encounters backlash in all joints, pose-dependent inertia, pose-dependent gravitational loads, pose-dependent hydraulic forces, pose- and velocity dependent centripetal and Coriolis forces as well as a nonlinear friction, which is temperature dependent and therefore potentially time varying. We supply the prepared dataset for black-box identification of the forward or the inverse robot dynamics. Additional to the data for black-box modelling, we supply high-frequency raw data and videos of each experiment. A baseline and figures of merit are defined to make results compareable across different identification methods.
Using industrial robots for machining applications in flexible manufacturing
processes lacks a high accuracy. The main reason for the deviation is the
flexibility of the gearbox. Secondary Encoders (SE) as an additional, high precision
angle sensor offer a huge potential of detecting gearbox deviations. This paper
aims to use SE to reduce gearbox compliances with a feed forward, adaptive
neural control. The control network is trained with a second network for system
identification. The presented algorithm is capable of online application and optimizes
the robot accuracy in a nonlinear simulation.
We present an identification benchmark data set for a full robot movement with an KUKA KR300 R2500 ultra SE industrial robot. It is a robot with a nominal payload capacity of 300 kg, a weight of 1120 kg and a reach of 2500mm. It exhibits 12 states accounting for position and velocity for each of the 6 joints. The robot encounters backlash in all joints, pose-dependent inertia, pose-dependent gravitational loads, pose-dependent hydraulic forces, pose- and velocity dependent centripetal and Coriolis forces as well as a nonlinear friction, which is temperature dependent and therefore potentially time varying. We supply the prepared dataset for black-box identification of the forward or the inverse robot dynamics. Additional to the data for black-box modelling, we supply high-frequency raw data and videos of each experiment. A baseline and figures of merit are defined to make results compareable across different identification methods.
A flexible operation of multiple robotic manipulators operating in a dynamic environment
requires online trajectory planning to ensure collision-free trajectories. In this work, we propose a real-time
capable motion control algorithm, based on nonlinear model predictive control, which accounts for static and
dynamic obstacles. The proposed algorithm is realized in a distributed scheme, where each robot optimizes its
own trajectory with respect to the related objective and constraints.We propose a novel approach for collision
avoidance between multiple robotic manipulators, where each robot accounts for the predicted movement of
the neighboring robots. Additionally, we propose a method to reliably detect and resolve deadlocks occurring
in a setup of multiple robotic manipulators.We validate our approach on pick and place scenarios involving
multiple robotic manipulators operating in a common workspace in a realistic simulation environment
set up in Gazebo. The robots are controlled using the Robot Operating System. Our approach scales up
to 4 manipulators and computes a path for each robot in a simultaneous pick and place operation in 94% of
all investigated cases without deadlock detection and 100 % of cases with the proposed deadlock resolution
algorithm. In contrast, the investigated conventional path planners, such as PRM, PRM*, CHOMP and RRTConnect,
successfully plan a trajectory in at most 54% of all investigated cases for a simultaneous operation
of 4 robotic manipulators hindering their application in setups of multiple manipulators.